Abstract
Achieving high efficiency on AI operators demands precise control over computation and data movement. However, existing scheduling languages are locked into specific compiler ecosystems, preventing fair comparison, reuse, and evaluation across frameworks. No unified interface currently decouples scheduling specification from code generation and measurement. We introduce XTC, a platform that unifies scheduling and performance evaluation across compilers. With its common API and reproducible measurement framework, XTC enables portable experimentation and accelerates research on optimization strategies.

AI Insights

• It decouples scheduling from code generation, enabling fair comparison, reproducible measurement, and rapid prototyping of optimization strategies. [3]
• TVM: an automated end-to-end optimizing compiler for deep learning Ansor: generating high-performance tensor programs for deep learning Aidge: a framework for building and optimizing compilers MLIR: A compiler infrastructure for the end of Moore's law XTC is a valuable tool for researchers and developers working on compiler frameworks. [3]
• XTC is a research platform for experimenting with scheduling and performance optimization across compiler frameworks. [2]

Why we are recommending this paper?
Due to your Interest in: AI Air Consumption

Abstract
Artificial intelligence (AI) is commonly depicted as transformative. Yet, after more than a decade of hype, its measurable impact remains modest outside a few high-profile scientific and commercial successes. The 2024 Nobel Prizes in Chemistry and Physics recognized AI's potential, but broader assessments indicate the impact to date is often more promotional than technical. We argue that while current AI may influence physics, physics has significantly more to offer this generation of AI. Current architectures - large language models, reasoning models, and agentic AI - can depend on trillions of meaningless parameters, suffer from distributional bias, lack uncertainty quantification, provide no mechanistic insights, and fail to capture even elementary scientific laws. We review critiques of these limits, highlight opportunities in quantum AI and analogue computing, and lay down a roadmap for the adoption of 'Big AI': a synthesis of theory-based rigour with the flexibility of machine learning.

AI Insights

• The article discusses the limitations and potential risks of relying solely on artificial intelligence (AI) for decision-making in various fields, including science, healthcare, and finance. [3]
• The author argues that AI should be used as a tool to augment human judgment and expertise, rather than replacing it. [3]
• The article highlights several examples of AI's limitations, such as its inability to understand the underlying physical laws governing complex systems, its susceptibility to bias and errors, and its lack of transparency and explainability. [3]
• Big Data Big Theory Digital Twins The article concludes that a balanced approach is necessary, combining the strengths of both human expertise and AI to achieve better outcomes in various fields. [3]
• Lack of transparency and explainability Susceptibility to bias and errors The article references several studies and papers on the limitations of AI in various fields, including physics, chemistry, and biology. [3]

Why we are recommending this paper?
Due to your Interest in: AI Air Consumption

Abstract
The rapid advancements in artificial intelligence (AI) present unique challenges for policymakers that seek to govern the technology. In this context, the Delphi method has become an established way to identify consensus and disagreement on emerging technological issues among experts in the field of futures studies and foresight. The aim of this article is twofold: first, it examines key tensions experts see in the development of AI governance in Europe, and second, it reflects on the Delphi method's capacity to inform anticipatory governance of emerging technologies like AI based on these insights. The analysis is based on the results of a two-round Policy Delphi study on the future of AI governance with European policymakers, researchers and NGOs, conducted in mid-2024. The Policy Delphi proved useful in revealing diverse perspectives on European AI governance, drawing out a consensus that future-proof AI regulation will likely depend more on practical implementation and enforcement of legislation than on its technical specifics or scope. Furthermore, the study identified a desirability-probability gap in AI governance: desirable policy directions, like greater citizen participation, were perceived as less probable and feasible. This highlights a tension between desirable regulatory oversight and the practical difficulty for regulation to keep up with technological change.

Why we are recommending this paper?
Due to your Interest in: AI Impacts on Society

Abstract
Agricultural regions in rural areas face damage from climate-related risks, including droughts, heavy rainfall, and shifting weather patterns. Prior research calls for adaptive risk-management solutions and decision-making strategies. To this end, artificial intelligence (AI), particularly agentic AI, offers a promising path forward. Agentic AI systems consist of autonomous, specialized agents capable of solving complex, dynamic tasks. While past systems have relied on single-agent models or have used multi-agent frameworks only for static functions, there is a growing need for architectures that support dynamic collaborative reasoning and context-aware outputs. To bridge this gap, we present AgroAskAI, a multi-agent reasoning system for climate adaptation decision support in agriculture, with a focus on vulnerable rural communities. AgroAskAI features a modular, role-specialized architecture that uses a chain-of-responsibility approach to coordinate autonomous agents, integrating real-time tools and datasets. The system has built-in governance mechanisms that mitigate hallucination and enable internal feedback for coherent, locally relevant strategies. The system also supports multilingual interactions, making it accessible to non-English-speaking farmers. Experiments on common agricultural queries related to climate adaptation show that, with additional tools and prompt refinement, AgroAskAI delivers more actionable, grounded, and inclusive outputs. Our experimental results highlight the potential of agentic AI for sustainable and accountable decision support in climate adaptation for agriculture.

AI Insights

• ChatGPT: A conversational AI model that provides general information on a wide range of topics. [3]
• The system's ability to analyze historical weather data and provide specific recommendations for farmers in Kitui, Kenya demonstrates its effectiveness in adapting to local climate conditions. [3]
• The AgroAskAI system provides a detailed and practical agricultural adaptation strategy tailored to the region of Kitui, Kenya. [2]
• CROPWAT: A software tool used for crop water management and irrigation planning. [1]

Why we are recommending this paper?
Due to your Interest in: AI for Social Equity

Abstract
Nowadays, Artificial Intelligence (AI), particularly Machine Learning (ML) and Large Language Models (LLMs), is widely applied across various contexts. However, the corresponding models often operate as black boxes, leading them to unintentionally act unfairly towards different demographic groups. This has led to a growing focus on fairness in AI software recently, alongside the traditional focus on the effectiveness of AI models. Through 26 semi-structured interviews with practitioners from different application domains and with varied backgrounds across 23 countries, we conducted research on fairness requirements in AI from software engineering perspective. Our study assesses the participants' awareness of fairness in AI / ML software and its application within the Software Development Life Cycle (SDLC), from translating fairness concerns into requirements to assessing their arising early in the SDLC. It also examines fairness through the key assessment dimensions of implementation, validation, evaluation, and how it is balanced with trade-offs involving other priorities, such as addressing all the software functionalities and meeting critical delivery deadlines. Findings of our thematic qualitative analysis show that while our participants recognize the aforementioned AI fairness dimensions, practices are inconsistent, and fairness is often deprioritized with noticeable knowledge gaps. This highlights the need for agreement with relevant stakeholders on well-defined, contextually appropriate fairness definitions, the corresponding evaluation metrics, and formalized processes to better integrate fairness into AI/ML projects.

AI Insights

• Participants showed an implicit understanding of fairness in AI/ML software, progressing from initial confusion with accuracy and robustness to recognizing its multiple dimensions. [2]

Why we are recommending this paper?
Due to your Interest in: AI for Social Fairness

Abstract
General-purpose conversational AI chatbots and AI companions increasingly provide young adolescents with emotionally supportive conversations, raising questions about how conversational style shapes anthropomorphism and emotional reliance. In a preregistered online experiment with 284 adolescent-parent dyads, youth aged 11-15 and their parents read two matched transcripts in which a chatbot responded to an everyday social problem using either a relational style (first-person, affiliative, commitment language) or a transparent style (explicit nonhumanness, informational tone). Adolescents more often preferred the relational than the transparent style, whereas parents were more likely to prefer transparent style than adolescents. Adolescents rated the relational chatbot as more human-like, likable, trustworthy and emotionally close, while perceiving both styles as similarly helpful. Adolescents who preferred relational style had lower family and peer relationship quality and higher stress and anxiety than those preferring transparent style or both chatbots. These findings identify conversational style as a key design lever for youth AI safety, showing that relational framing heightens anthropomorphism, trust and emotional closeness and can be especially appealing to socially and emotionally vulnerable adolescents, who may be at increased risk for emotional reliance on conversational AI.

AI Insights

• Adolescents favored relational style over transparent style in AI chatbots, seeing the former as more human-like, likable, trustworthy, and emotionally close. [3]
• The relational style was associated with increased anthropomorphism, which may enhance trust, likability, and emotional closeness. [3]
• Adolescents with lower relationship quality and higher distress were more drawn to the relational chatbot, suggesting a link between social deprivation and AI use. [3]
• Relational style: using language and responses that simulate social relationships and emotional support, making the chatbot feel more like a social other. [3]
• The study provides insights into how adolescents and parents evaluate relational versus transparent conversational styles in AI chatbots, highlighting the importance of balancing anthropomorphic language with clear boundaries and transparency. [3]
• The findings suggest that relational style is attractive to adolescents and acceptable to many parents, but also raises concerns about emotional reliance on AI and displacement of human relationships. [3]
• The study highlights the importance of designing AI chatbots that balance anthropomorphic language with clear boundaries and transparency. [2]
• Parents were more likely to prefer transparent style, emphasizing safety and boundary clarity, but many still preferred relational style for their adolescents. [1]

Why we are recommending this paper?
Due to your Interest in: AI for Social Good

Abstract
Cutting-edge agentic AI systems are built on foundation models that can be adapted to plan, reason, and interact with external tools to perform increasingly complex and specialized tasks. As these systems grow in capability and scope, adaptation becomes a central mechanism for improving performance, reliability, and generalization. In this paper, we unify the rapidly expanding research landscape into a systematic framework that spans both agent adaptations and tool adaptations. We further decompose these into tool-execution-signaled and agent-output-signaled forms of agent adaptation, as well as agent-agnostic and agent-supervised forms of tool adaptation. We demonstrate that this framework helps clarify the design space of adaptation strategies in agentic AI, makes their trade-offs explicit, and provides practical guidance for selecting or switching among strategies during system design. We then review the representative approaches in each category, analyze their strengths and limitations, and highlight key open challenges and future opportunities. Overall, this paper aims to offer a conceptual foundation and practical roadmap for researchers and practitioners seeking to build more capable, efficient, and reliable agentic AI systems.

AI Insights

• The four adaptation paradigms in agentic AI are A1 (agent adaptation with tool-execution result as signal), A2 (agent adaptation with agent output as signal), T1 (tool adaptation with agent output as signal), and T2 (tool adaptation with agent output as signal). [3]
• A1 methods use the actual outcomes of external tool invocations as feedback to refine an agent's behavior. [3]
• Recent A1 methods include Toolformer, TRICE, Gorilla, ToolAlpaca, and others, which have achieved state-of-the-art performance on various tasks such as question-answering, math reasoning, and web search. [3]
• The RLVR (Reinforcement Learning with Value Regularization) framework is a key component of many recent A1 methods, allowing for more efficient learning and better generalization. [3]
• A2 methods focus on evaluating an agent's own outputs, rather than relying on tool execution results as feedback. [3]
• The development timeline of A1 methods shows a shift from earlier methods such as SFT (Self-Modifying Task) and DPO (Dynamic Policy Optimization) to more recent RLVR-based methods. [3]
• Recent A1 methods have achieved state-of-the-art performance on various tasks, including question-answering, math reasoning, web search, and text-to-SQL. [3]
• The development timeline of A1 methods shows a rapid growth in research, with many new methods being proposed between 2023 and 2025. [2]
• T1 and T2 methods involve adapting tools based on the agent's output, which can be useful in scenarios where the agent needs to interact with multiple tools or environments. [1]

Why we are recommending this paper?
Due to your Interest in: AI on Air

Abstract
Generative Artificial Intelligence (GenAI) is rapidly reshaping how knowledge is produced and validated in education. Rather than adding another digital tool, large language models reconfigure reading, writing, and coding into hybrid human-AI workflows, raising concerns about epistemic automation, cognitive offloading, and the de-professiona\-lisation of teachers. This paper proposes \emph{Cyber Humanism in Education} as a framework for reclaiming human agency in this landscape. We conceptualise AI-enabled learning environments as socio-technical infrastructures co-authored by humans and machines, and position educators and learners as epistemic agents and \emph{algorithmic citizens} who have both the right and the responsibility to shape these infrastructures. We articulate three pillars for cyber-humanist design, \emph{reflexive competence}, \emph{algorithmic citizenship}, and \emph{dialogic design}, and relate them to major international digital and AI competence frameworks. We then present higher-education case studies that operationalise these ideas through \emph{prompt-based learning} and a new \emph{Conversational AI Educator} certification within the EPICT ecosystem. The findings show how such practices can strengthen epistemic agency while surfacing tensions around workload, equity, and governance, and outline implications for the future of AI-rich, human-centred education.

AI Insights

• Cyber Humanism emphasizes the importance of reflexive competence, algorithmic citizenship, and dialogic design in human-AI interaction. [3]
• Cyber Humanism: A design lens that foregrounds learners and educators as epistemic agents and algorithmic citizens who can interrogate, shape, and co-govern AI-rich learning environments through reflexive competence, algorithmic citizenship, and dialogic design. [3]
• Algorithmic citizenship: The participation of learners and educators in shaping the rules, norms, and infrastructures of AI use in their institutions and communities. [3]
• The integration of generative AI in education requires a shift from viewing AI as an external disruptive force or neutral tool to understanding it as part of the cognitive and institutional infrastructures of learning. [3]
• The paper highlights gaps in existing digital and AI competence frameworks concerning infrastructural participation, everyday enactment of algorithmic citizenship, and the role of natural language as a modelling medium in human-AI interaction. [2]
• The paper discusses the integration of generative AI in education and proposes a design lens called Cyber Humanism that foregrounds learners and educators as epistemic agents and algorithmic citizens. [1]

Why we are recommending this paper?
Due to your Interest in: AI on Education

Abstract
Both student retention in higher education and artificial intelligence governance face a common structural challenge: the application of linear regulatory frameworks to complex adaptive systems. Risk-based approaches dominate both domains, yet systematically fail because they assume stable causal pathways, predictable actor responses, and controllable system boundaries. This paper extracts transferable methodological principles from CAPIRE (Curriculum, Archetypes, Policies, Interventions & Research Environment), an empirically validated framework for educational analytics that treats student dropout as an emergent property of curricular structures, institutional rules, and macroeconomic shocks. Drawing on longitudinal data from engineering programmes and causal inference methods, CAPIRE demonstrates that well-intentioned interventions routinely generate unintended consequences when system complexity is ignored. We argue that five core principles developed within CAPIRE - temporal observation discipline, structural mapping over categorical classification, archetype-based heterogeneity analysis, causal mechanism identification, and simulation-based policy design - transfer directly to the challenge of governing AI systems. The isomorphism is not merely analogical: both domains exhibit non-linearity, emergence, feedback loops, strategic adaptation, and path dependence. We propose Complex Systems AI Governance (CSAIG) as an integrated framework that operationalises these principles for regulatory design, shifting the central question from "how risky is this AI system?" to "how does this intervention reshape system dynamics?" The contribution is twofold: demonstrating that empirical lessons from one complex systems domain can accelerate governance design in another, and offering a concrete methodological architecture for complexity-aware AI regulation.

AI Insights

• The development of CAPIRE generated insights that extend beyond the specific domain of educational analytics. [3]
• Complex adaptive system: A system that exhibits non-linearity, emergence, feedback, adaptation, and path dependence. [3]
• Several methodological lessons emerged from iterative engagement with data, models, and institutional stakeholders. [2]

Why we are recommending this paper?
Due to your Interest in: AI on Education

Abstract
AI Epidemiology is a framework for governing and explaining advanced AI systems by applying population-level surveillance methods to AI outputs. The approach mirrors the way in which epidemiologists enable public health interventions through statistical evidence before molecular mechanisms are understood. This bypasses the problem of model complexity which plagues current interpretability methods (such as SHAP and mechanistic interpretability) at the scale of deployed models. AI Epidemiology achieves this population-level surveillance by standardising capture of AI-expert interactions into structured assessment fields: risk level, alignment score, and accuracy score. These function as exposure variables which predict output failure through statistical associations, much like cholesterol and blood pressure act as exposure variables predicting cardiac events. Output-failure associations are subsequently validated against expert overrides and real-world outcomes. The framework places zero burden on experts and provides automatic audit trails by passively tracking expert convergence and divergence with AI recommendations. Since it analyses outputs rather than internal model computations, it also provides governance continuity when institutions update models and switch vendors. Finally, by providing reliability scores and semantic assessments (e.g. 'this recommendation resembles 500 cases overridden by experts due to guideline violations'), it enables experts and institutions to detect unreliable AI outputs before they cause harm. This democratises AI oversight by enabling domain experts to govern AI systems without requiring machine learning expertise.

AI Insights

• Tracelayer, a key component of Logia, stores and analyzes large datasets of AI-expert interactions to refine reliability scores and provide structured failure cases for interpretability research. [3]
• Logia's dual assessment framework combines risk level (consequence severity) with reliability score (output failure probability) to enable institutions to identify both where models fail and which failures matter most. [3]
• Reliability Score: A measure of output trustworthiness based on population patterns of expert intervention and adverse outcomes. [3]
• Logia's AI-Epidemiology approach enables real-time identification and correction of problematic outputs, reducing the risk of harm caused by biased or inaccurate models. [3]
• Logia's dual assessment framework enables institutions to choose which outputs are flagged for review based on their risk tolerance. [3]
• The article does not provide a clear explanation of how Logia's approach can be scaled up for large-scale applications. [3]
• Logia's AI-Epidemiology approach combines population-level data with expert oversight to identify and correct problematic outputs in real-time. [2]

Why we are recommending this paper?
Due to your Interest in: AI on Healthcare

Abstract
Designing sustainable medical devices requires balancing environmental, economic, and social demands, yet trade-offs across these pillars are difficult to identify using manual assessment alone. Current methods depend heavily on expert judgment, lack standardisation, and struggle to integrate diverse lifecycle data, which leads to overlooked conflicts and inconsistent evaluations. This paper introduces an AI-driven framework that automates conflict detection. Machine learning and natural language processing are used to extract trade-offs from design decisions, while Multi-Criteria Decision Analysis (MCDA) quantifies their magnitude through a composite sustainability score. The approach improves consistency, reduces subjective bias, and supports early design decisions. The results demonstrate how AI-assisted analysis provides scalable, data-driven support for sustainability evaluation in medical device development.

Why we are recommending this paper?
Due to your Interest in: AI on Healthcare